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Making Parameter Dependencies of Time‐Series Segmentation Visually Understandable
Author(s) -
Eichner Christian,
Schumann Heidrun,
Tominski Christian
Publication year - 2020
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13894
Subject(s) - computer science , segmentation , toolbox , series (stratigraphy) , context (archaeology) , visualization , domain (mathematical analysis) , market segmentation , artificial intelligence , dependency (uml) , scale space segmentation , data mining , sorting , parameter space , visual analytics , pattern recognition (psychology) , machine learning , image segmentation , algorithm , mathematics , paleontology , mathematical analysis , statistics , marketing , business , biology , programming language
This work presents an approach to support the visual analysis of parameter dependencies of time‐series segmentation. The goal is to help analysts understand which parameters have high influence and which segmentation properties are highly sensitive to parameter changes. Our approach first derives features from the segmentation output and then calculates correlations between the features and the parameters, more precisely, in parameter subranges to capture global and local dependencies. Dedicated overviews visualize the correlations to help users understand parameter impact and recognize distinct regions of influence in the parameter space. A detailed inspection of the segmentations is supported by means of visually emphasizing parameter ranges and segments participating in a dependency. This involves linking and highlighting, and also a special sorting mechanism that adjusts the visualization dynamically as users interactively explore individual dependencies. The approach is applied in the context of segmenting time series for activity recognition. Informal feedback from a domain expert suggests that our approach is a useful addition to the analyst's toolbox for time‐series segmentation.